Grant: Discovery Grants Program - Individual
Concordia University
$1,652.00
Reliability analysis and maintenance optimization based on advanced prognostics methodologies
EF 2013-14
Résumé
Reliability measures the ability of a component or system performing its intended functions, and it is one of the most critical performance measures of engineering systems such as aircraft systems, manufacturing systems and wind power systems. Maintenance optimization aims to improve reliability and reduce the overall cost. In conventional reliability analysis, failure data are mainly analyzed using statistical methods, and reliability performance is estimated for component population rather than individual units. The availability of condition monitoring data, such as vibration and acoustic data, make it possible to obtain a far more accurate reliability prediction for individual components, and there's great potential to subsequently achieve more effective system reliability analysis and maintenance management. The long-term objective of this research program is to develop an integrated framework and a suite of methods and tools for accurate reliability analysis and effective maintenance and logistics optimization based on accurate prognostics information, and apply them to engineering systems particularly renewable energy, manufacturing, and aerospace systems. The short-term objectives are to greatly advance the highly promising integrated prognostics so that its methods and tools can be applied to complex equipments in broad industry applications, establish a unified system reliability analysis framework and methods based on component prognostics information, generate more accurate and efficient CBM optimization methods so that they are applicable to complex systems, and develop effective methods for wind energy system reliability enhancement considering their special time-varying loading characteristics and complex structures. Results from this research will improve reliability, safety and cost-effectiveness of engineering systems, and generate major economic benefits for Canadian renewable energy, manufacturing, aerospace, and oil and gas industries. Highly qualified personnel will be trained.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
355586-2013
Name nom
Tian, Zhigang
Department département
Concordia Institute for Information Systems Engineering (CIISE)
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2013
Competition year année de concours
2013
Award amount
1652
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1509
Committee name en
Civil, Industrial and Systems Engineering
Committee name fr
Génie civil, industriel et des systèmes
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
1601
Research subject group en
Industrial engineering
Research subject group fr
Génie industriel
Research subject en
Operations research and management science
Research subject fr
Recherche opérationnelle et science de la gestion
Partie
1
Nb partie
5
Application title
Reliability analysis and maintenance optimization based on advanced prognostics methodologies
Keyword
NSERC
Application summary
Reliability measures the ability of a component or system performing its intended functions, and it is one of the most critical performance measures of engineering systems such as aircraft systems, manufacturing systems and wind power systems. Maintenance optimization aims to improve reliability and reduce the overall cost. In conventional reliability analysis, failure data are mainly analyzed using statistical methods, and reliability performance is estimated for component population rather than individual units. The availability of condition monitoring data, such as vibration and acoustic data, make it possible to obtain a far more accurate reliability prediction for individual components, and there's great potential to subsequently achieve more effective system reliability analysis and maintenance management. The long-term objective of this research program is to develop an integrated framework and a suite of methods and tools for accurate reliability analysis and effective maintenance and logistics optimization based on accurate prognostics information, and apply them to engineering systems particularly renewable energy, manufacturing, and aerospace systems. The short-term objectives are to greatly advance the highly promising integrated prognostics so that its methods and tools can be applied to complex equipments in broad industry applications, establish a unified system reliability analysis framework and methods based on component prognostics information, generate more accurate and efficient CBM optimization methods so that they are applicable to complex systems, and develop effective methods for wind energy system reliability enhancement considering their special time-varying loading characteristics and complex structures. Results from this research will improve reliability, safety and cost-effectiveness of engineering systems, and generate major economic benefits for Canadian renewable energy, manufacturing, aerospace, and oil and gas industries. Highly qualified personnel will be trained.
